Integration of in Silico and in Vitro Tools for Scaffold Optimization during Drug Discovery: Predicting P-Glycoprotein Efflux
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- 作者：Prashant V. Desai ; Geri A. Sawada ; Ian A. Watson ; Thomas J. Raub
- 刊名：Molecular Pharmaceutics
- 出版年：2013
- 出版时间：April 1, 2013
- 年：2013
- 卷：10
- 期：4
- 页码：1249-1261
- 全文大小：484K
- 年卷期：v.10,no.4(April 1, 2013)
- ISSN：1543-8392
文摘
In silico tools are regularly utilized for designing and prioritizing compounds to address challenges related to drug metabolism and pharmacokinetics (DMPK) during the process of drug discovery. P-Glycoprotein (P-gp) is a member of the ATP-binding cassette (ABC) transporters with broad substrate specificity that plays a significant role in absorption and distribution of drugs that are P-gp substrates. As a result, screening for P-gp transport has now become routine in the drug discovery process. Typically, bidirectional permeability assays are employed to assess in vitro P-gp efflux. In this article, we use P-gp as an example to illustrate a well-validated methodology to effectively integrate in silico and in vitro tools to identify and resolve key barriers during the early stages of drug discovery. A detailed account of development and application of in silico tools such as simple guidelines based on physicochemical properties and more complex quantitative structure鈥揳ctivity relationship (QSAR) models is provided. The tools were developed based on structurally diverse data for more than 2000 compounds generated using a robust P-gp substrate assay over the past several years. Analysis of physicochemical properties revealed a significantly lower proportion (<10%) of P-gp substrates among the compounds with topological polar surface area (TPSA) <60 脜2 and the most basic cpKa <8. In contrast, this proportion of substrates was greater than 75% for compounds with TPSA >60 脜2 and the most basic cpKa >8. Among the various QSAR models evaluated to predict P-gp efflux, the Bagging model provided optimum prediction performance for prospective validation based on chronological test sets. Four sequential versions of the model were built with increasing numbers of compounds to train the models as new data became available. Except for the first version with the smallest training set, the QSAR models exhibited robust prediction profiles with positive prediction values (PPV) and negative prediction values (NPV) exceeding 80%. The QSAR model demonstrated better concordance with the manual P-gp substrate assay than an automated P-gp substrate screen. The in silico and the in vitro tools have been effectively integrated during early stages of drug discovery to resolve P-gp-related challenges exemplified by several case studies. Key learning based on our experience with P-gp can be widely applicable across other DMPK-related challenges.